mitools is useful too, and I can vouch for mice. mice is easy to use,
and easy to write new imputation methods too. So it is also very flexible.
Simon.
On 30/06/10 15:31, Jeremy Miles wrote:
Hi Daniel
First, newer versions of SPSS have dramatically improved their ability
to do stuff with missing data - I believe it's an additional module,
and in SPSS-world, each additional module = $$$.
Analyzing missing data is a 3 step process. First, you impute,
creating multiple datasets, then you analyze each dataset in the
conventional way, then you combine the results. There are two (that
I know of) packages for imputaton - these are mi and mice. rseek.org
will find them for you.
Hope that helps,
Jeremy
On 29 June 2010 22:14, Daniel Chen<n...@pushih.com> wrote:
Hi,
I am a long time SPSS user but new to R, so please bear with me if my
questions seem to be too basic for you guys.
I am trying to figure out how to analyze survey data using logistic
regression with multiple imputation.
I have a survey data of about 200,000 cases and I am trying to predict the
odds ratio of a dependent variable using 6 categorical independent variables
(dummy-coded). Approximatively 10% of the cases (~20,000) have missing data
in one or more of the independent variables. The percentage of missing
ranges from 0.01% to 10% for the independent variables.
My current thinking is to conduct a logistic regression with multiple
imputation, but I don't know how to do it in R. I searched the web but
couldn't find instructions or examples on how to do this. Since SPSS is
hopeless with missing data, I have to learn to do this in R. I am new to R,
so I would really appreciate if someone can show me some examples or tell me
where to find resources.
Thank you!
Daniel
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